Ideal Answers to Chapter 4 Questions
QUESTION 4.1. As shown below, students did seem to be overrepresented at universities whose names
included their surnames. This result was significant, 2 (1) = 3.97, p = .047. Because the study only used
two names we cannot be sure whether this result was driven by only one name or by both. (We would
need at least one additional name, preferably many names, to know exactly which names showed the
effect.)
University Name
Student Surname
Brown
Lewis
Brown University
42
14
Lewis University
9
9
This finding is consistent with implicit egotism, which states that people prefer people, places and things
that resemble their own names. We can rule out one form of reverse causality. It’s extremely unlikely
that attending Brown or Lewis University made anyone change his or her surname to match the name of
the university.
QUESTION 4.2. It is possible that these results are the product of a subtle confound. One possible
confound is that the results may reflect some kind of “grandfather effect.” If, for whatever reason, a lot
of people named Brown settled long ago in the place where Brown university is now located, this
preponderance of Browns might have made it more likely that a Brown would become wealthy enough
to found a university. If this preponderance still exists and many of Brown’s recruits are regional, this
could conceivably generate this kind of name-matching effect. Perhaps more worrisome, if Brown had
any grandchildren, or great, great, great grandchildren, it’s likely that many of them would be named
Brown, and they’d have quite an advantage at being admitted to Brown. Though less likely it’s also
possible that Brown is, on average, a wealthier surname than Lewis. If Brown is also a more expensive
or elite university than Lewis, then this is a worrisome confound.
QUESTION 4.3.Like the university choice study, the marriage study yielded results consistent with
implicit egotism. As shown below, people were more likely to marry other people whose surnames
began with the same letter as their own, 2 (1) =4.26, p = .039. Expected frequencies appear in
parenthesis.
Groom’s Surname Initial
Bride’s Maiden Initial
C
D
C
17 (13.2)
9 (12.8)
D
10 (13.8)
17 (13.2)
Like the first study, these findings are immune to some forms of reverse causality. It’s unlikely, for
example, that bride’s changed their maiden names to Cooper after deciding to marry someone named
Clarke.
QUESTION 4.4. This archival study also suffers from some potential problems. First, although this
sample seemed pretty ethnically homogenous, it is possible, in principle, that the letter name matching
effects we observed are really some kind of ethnic matching effect in disguise. If Scottish names were
more likely to begin with C and German names more likely to start with D, for example, this ethnic
matching effect could masquerade as implicit egotism. Second, a few of these couples had the same last
name. It’s possible that this was a coding error (if someone erroneously entered a woman’s married
name where her maiden name should have gone). Third, it seems unlikely, but is conceivable, that
there are SES (socioeconomic status) differences between people whose surnames begin with C and D.
SES matching could operate much like ethnic matching. Finally, the study should have included all of the
names in the complete set of marriage records. It would be much more impressive (and external
validity would be much higher) if we were to see an average name-letter matching effect across all 26
letters of the alphabet rather than merely for two letters. In fact, this effect for only two letters could
merely reflect sampling error.